Sensitivity to Unobserved Confounding in Studies with Factor-Structured Outcomes

• 成果类型: Article
• 署名作者: Zheng, Jiajing; Wu, Jiaxi; D'Amour, Alexander; Franks, Alexander
• 署名单位: University of California System; University of California Santa Barbara; Alphabet Inc.; Google Incorporated
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2240053
• 发表日期: 2024
• 页码: 2026-2037
• 关键词: multiple outcomes; alcohol intake; models; HEALTH

摘要

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES). Supplementary materials for this article are available online.